CN109246296A - A kind of mobile phone safe information generates and storage method - Google Patents
A kind of mobile phone safe information generates and storage method Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
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- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
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Abstract
The invention discloses a kind of generation of mobile phone safe information and storage methods, belong to information technology field, user generates or receives the file information；Using the photographic intelligence as photo training sample, input SVM classifier model is trained, SVM classifier model after photographic intelligence to be identified input training is classified, if classification results are sensitive information, then the photographic intelligence is encrypted, it generates photo security information and stores to local cipher photo stocking system, otherwise do not generate security information；Using the document information as document training sample, input neural network model is trained, neural network model after document information to be identified input training is classified, if classification results are sensitive information, then the document information is encrypted, generates document security information, and is stored to local cipher document stocking system, otherwise security information is not generated, the security performance of the information in user mobile phone can be effectively improved using the present invention.
Description
Technical field
The present invention relates to information technology fields, and in particular to a kind of mobile phone safe information generates and storage method.
Background technique
Currently, popularizing with smart phone, mobile phone is more and more intelligent, and what people stored on mobile phone is also no longer several
Telephone number before year, and have a large amount of photo, log, browsing record, voucher etc..
People are frequently utilized that wechat chat, with Alipay payment, mobile office etc., and mobile phone is more to be used as one to enter in fact
Mouth either medium exists.Meanwhile a large amount of secondary verifying relies on short message verification code and therefore for malice APP, only needs in fact
Accomplish that abduction/upload information can take many accounts, needless to say there are also false shutdown to monitor voice etc.
Deng.And the APP distribution of Android is so unified unlike iOS, therefore you at will under one crack an edition game, it is likely that just
Being repacked by people placed back door.For pseudo-base station, user mobile phone flow can be kidnapped, short message sniff；For WIFI,
Salesman tells that WIFI is broken in your shop when being permitted to have a meal, and you normally have connected, and discovery account number is stolen within second day.
Most files is stored in local in mobile phone, and local file not will do it encryption, mobile phone lose or
When person repairs, criminal is easy to crack cell phone password or is stolen the file in mobile phone using other modes, makes user's
Privacy leakage.
Summary of the invention
It is an object of the invention to: a kind of mobile phone safe information is provided and is generated and storage method, solves current mobile phone text
The technical problem that part is easily stolen, safety is not high.
The technical solution adopted by the invention is as follows:
A kind of mobile phone safe information generating method, comprising the following steps:
Step 1: user generates or receives the file information；
Step 2: judging that the file information for photographic intelligence or document information, if photographic intelligence, then jumps to step
Rapid 3, if document information, then go to step 5；
Step 3: using the photographic intelligence as photo training sample, inputting SVM classifier model and be trained, work as classification
When accuracy rate is greater than threshold value, the SVM classifier model after being trained；
Step 4: the SVM classifier model after photographic intelligence to be identified input training being classified, if classification results are
Sensitive information then encrypts the photographic intelligence, generates photo security information, does not otherwise generate security information.
Step 5: using the document information as document training sample, inputting neural network model and be trained, work as classification
When accuracy rate is greater than threshold value, the neural network model after being trained；
Step 6: the neural network model after document information to be identified input training being classified, if classification results are quick
Feel information, then the document information is encrypted, generates document security information, otherwise do not generate security information.
Further, in the step 1, it is the file information that user is autonomously formed that user, which generates the file information, including is clapped
It takes the photograph, write and makes.
Further, in the step 3, using photographic intelligence as when photo training sample, need to photographic intelligence carry out
Pretreatment, pretreated step are as follows:
Step 31: median filtering is carried out to photographic intelligence；
Step 32: limitation contrast histogram equalization processing is carried out to the image after median filtering；
Step 33: chromatic value adjustment is carried out to the image that step 32 obtains.
Further, in the step 3, SVM classifier model is trained using photo training sample specific step
Suddenly are as follows:
Step 34: photo training sample being inputted into SVM classifier model, obtains classification results；
Step 35: user selects the true classification results of the photo training sample；
Step 36:SVM sorter model is according to the true classification results adjusting parameter；
Step 37: when classification accuracy rate is greater than threshold value, completing training, the SVM classifier model after being trained.
Further, in the step 5, the specific steps that are trained using document training sample neural network model
Are as follows:
Step 51: document training sample being inputted into neural network model, obtains classification results；
Step 52: user selects the true classification results of the document training sample；
Step 53: neural network model is according to the true classification results adjusting parameter；
Step 54: when classification accuracy rate is greater than threshold value, completing training, the neural network model after being trained.
Further, the encryption method used in the step 4 and step 6 is one of SM2 and SM3.
A kind of mobile phone safe information storing method, comprising the following steps:
Step 1: user generates or receives the file information to be identified,
Step 2: judging the file information classification, if photographic intelligence to be identified, then 3 are gone to step, if text to be identified
Shelves information then gos to step 4；
Step 3: by the SVM classifier model after photographic intelligence to be identified input training, classification results are obtained, if classification knot
Fruit is sensitive information, then encrypts to photo, and generates photo security information, and the photo security information is stored in local
Encryption photo stocking system is then stored in non-encrypted photo stocking system if non-sensitive information；
Step 4: by the neural network model after document information to be identified input training, classification results are obtained, if classification knot
Fruit is sensitive information, then encrypts to document, and generates document security information, and the document security information is stored in local
Encrypted document stocking system is then stored in non-encrypted document stocking system if non-sensitive information.
In conclusion by adopting the above-described technical solution, the beneficial effects of the present invention are:
Using security information generation of the invention and storage method, the peace of the information in user mobile phone can be effectively improved
Full performance；At the initial stage of using, the storage of each film,fault or document can be carried out SVM classifier model or neural network model
Training, need user's selection sort as a result, but when the accuracy of category of model result reaches requirement, classify without user
As a result selection is able to achieve automatic classified storage.
Detailed description of the invention
Examples of the present invention will be described by way of reference to the accompanying drawings, in which:
Fig. 1 is overall flow figure of the invention.
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed invention to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
Range, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
It elaborates below with reference to Fig. 1 to the present invention.
A kind of mobile phone safe information generating method, comprising the following steps:
Step 1: user generates or receives the file information；
Step 2: judging that the file information for photographic intelligence or document information, if photographic intelligence, then jumps to step
Rapid 3, if document information, then go to step 5；
Step 3: using the photographic intelligence as photo training sample, inputting SVM classifier model and be trained, work as classification
When accuracy rate is greater than threshold value, the SVM classifier model after being trained；
Step 4: the SVM classifier model after photographic intelligence to be identified input training being classified, if classification results are
Sensitive information then encrypts the photographic intelligence, generates photo security information, does not otherwise generate security information.
Step 5: using the document information as document training sample, inputting neural network model and be trained, work as classification
When accuracy rate is greater than threshold value, the neural network model after being trained；
Step 6: the neural network model after document information to be identified input training being classified, if classification results are quick
Feel information, then the document information is encrypted, generates document security information, otherwise do not generate security information.
Further, in the step 1, it is the file information that user is autonomously formed that user, which generates the file information, including is clapped
It takes the photograph, write and makes.
Further, in the step 3, using photographic intelligence as when photo training sample, need to photographic intelligence carry out
Pretreatment, pretreated step are as follows:
Step 31: median filtering is carried out to photographic intelligence；
Step 32: limitation contrast histogram equalization processing is carried out to the image after median filtering；
Step 33: chromatic value adjustment is carried out to the image that step 32 obtains.
Further, in the step 3, SVM classifier model is trained using photo training sample specific step
Suddenly are as follows:
Step 34: photo training sample being inputted into SVM classifier model, obtains classification results；
Step 35: user selects the true classification results of the photo training sample；
Step 36:SVM sorter model is according to the true classification results adjusting parameter；
Step 37: when classification accuracy rate is greater than threshold value, completing training, the SVM classifier model after being trained.
Further, in the step 5, the specific steps that are trained using document training sample neural network model
Are as follows:
Step 51: document training sample being inputted into neural network model, obtains classification results；
Step 52: user selects the true classification results of the document training sample；
Step 53: neural network model is according to the true classification results adjusting parameter；
Step 54: when classification accuracy rate is greater than threshold value, completing training, the neural network model after being trained.
Further, the encryption method used in the step 4 and step 6 is one of SM2 and SM3.
A kind of mobile phone safe information storing method, comprising the following steps:
Step 1: user generates or receives the file information to be identified,
Step 2: judging the file information classification, if photographic intelligence to be identified, then 3 are gone to step, if text to be identified
Shelves information then gos to step 4；
Step 3: by the SVM classifier model after photographic intelligence to be identified input training, classification results are obtained, if classification knot
Fruit is sensitive information, then encrypts to photo, and generates photo security information, and the photo security information is stored in local
Encryption photo stocking system is then stored in non-encrypted photo stocking system if non-sensitive information；
Step 4: by the neural network model after document information to be identified input training, classification results are obtained, if classification knot
Fruit is sensitive information, then encrypts to document, and generates document security information, and the document security information is stored in local
Encrypted document stocking system is then stored in non-encrypted document stocking system if non-sensitive information.
Specific embodiment
A kind of mobile phone safe information generating method, comprising the following steps:
Step 1: user generates or receives the file information, i.e. user triggers security information when saving the file information and generates machine
System；It is the file information that user is autonomously formed that user, which generates the file information, including shoots, writes and make.
Step 2: judging that the file information for photographic intelligence or document information, if photographic intelligence, then jumps to step
Rapid 3, if document information, then go to step 5；
Step 3: using the photographic intelligence as photo training sample, inputting SVM classifier model and be trained, work as classification
When accuracy rate is greater than threshold value, the SVM classifier model after being trained；
Using photographic intelligence as when photo training sample, need to pre-process photographic intelligence, pretreated step are as follows:
Step 31: median filtering is carried out to photographic intelligence；
Step 32: limitation contrast histogram equalization processing is carried out to the image after median filtering；
Step 33: chromatic value adjustment is carried out to the image that step 32 obtains.
The specific steps that SVM classifier model is trained using photo training sample are as follows:
Step 34: photo training sample being inputted into SVM classifier model, obtains classification results；
Step 35: user selects the true classification results of the photo training sample；
Step 36:SVM sorter model is according to the true classification results adjusting parameter；
Step 37: all frequency of training in comprehensive front are completed training, are instructed when classification accuracy rate is greater than 95%
SVM classifier model after white silk.
Step 4: the SVM classifier model after photographic intelligence to be identified input training being classified, if classification results are
Sensitive information then encrypts the photographic intelligence, generates photo security information, does not otherwise generate security information, and use adds
Decryption method is SM2.
Step 5: using the document information as document training sample, inputting neural network model and be trained, work as classification
When accuracy rate is greater than threshold value, the neural network model after being trained；
The specific steps being trained using document training sample neural network model are as follows:
Step 51: document training sample being inputted into neural network model, obtains classification results；
Step 52: user selects the true classification results of the document training sample；
Step 53: neural network model is according to the true classification results adjusting parameter；
Step 54: all frequency of training in comprehensive front are completed training, are instructed when classification accuracy rate is greater than 95%
Neural network model after white silk.
Step 6: the neural network model after document information to be identified input training being classified, if classification results are quick
Feel information, then the document information is encrypted, generates document security information, otherwise do not generate security information, the encryption of use
Method is SM2.
When being trained to SVM classifier model and neural network, initially use training sample is each user storage
The generation of automatic classification and security information can be just realized after the completion of the content deposited, only model training.
A kind of mobile phone safe information storing method, comprising the following steps:
Step 1: user generates or receives the file information to be identified,
Step 2: judging the file information classification, if photographic intelligence to be identified, then 3 are gone to step, if text to be identified
Shelves information then gos to step 4；
Step 3: by the SVM classifier model after photographic intelligence to be identified input training, classification results are obtained, if classification knot
Fruit is sensitive information, then encrypts to photo, and generates photo security information, and the photo security information is stored in local
Encryption photo stocking system is then stored in non-encrypted photo stocking system if non-sensitive information；
Step 4: by the neural network model after document information to be identified input training, classification results are obtained, if classification knot
Fruit is sensitive information, then encrypts to document, and generates document security information, and the document security information is stored in local
Encrypted document stocking system is then stored in non-encrypted document stocking system if non-sensitive information.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.It should also be noted that similar label and letter exist
Similar terms are indicated in following attached drawing, therefore, once being defined in a certain Xiang Yi attached drawing, are then not required in subsequent attached drawing
It is further defined and explained.
Claims (7)
1. a kind of mobile phone safe information generating method, it is characterised in that: the following steps are included:
Step 1: user generates or receives the file information；
Step 2: judge the file information for photographic intelligence or document information, if photographic intelligence, then gos to step 3,
If document information, then 5 are gone to step；
Step 3: using the photographic intelligence as photo training sample, inputting SVM classifier model and be trained, when classification is accurate
When rate is greater than threshold value, the SVM classifier model after being trained；
Step 4: the SVM classifier model after photographic intelligence to be identified input training being classified, if classification results are sensitivity
Information then encrypts the photographic intelligence, generates photo security information, does not otherwise generate security information.
Step 5: using the document information as document training sample, inputting neural network model and be trained, when classification is accurate
When rate is greater than threshold value, the neural network model after being trained；
Step 6: the neural network model after document information to be identified input training being classified, if classification results are sensitive letter
Breath, then encrypt the document information, generates document security information, does not otherwise generate security information.
2. a kind of mobile phone safe information generating method according to claim 1, it is characterised in that: in the step 1, user
Generating the file information is the file information that user is autonomously formed, including shoots, writes and make.
3. a kind of mobile phone safe information generating method according to claim 1, it is characterised in that: in the step 3, will shine
It when piece information is as photo training sample, needs to pre-process photographic intelligence, pretreated step are as follows:
Step 31: median filtering is carried out to photographic intelligence；
Step 32: limitation contrast histogram equalization processing is carried out to the image after median filtering；
Step 33: chromatic value adjustment is carried out to the image that step 32 obtains.
4. a kind of mobile phone safe information generating method according to claim 1, it is characterised in that: in the step 3, utilize
The specific steps that photo training sample is trained SVM classifier model are as follows:
Step 34: photo training sample being inputted into SVM classifier model, obtains classification results；
Step 35: user selects the true classification results of the photo training sample；
Step 36:SVM sorter model is according to the true classification results adjusting parameter；
Step 37: when classification accuracy rate is greater than threshold value, completing training, the SVM classifier model after being trained.
5. a kind of mobile phone safe information generating method according to claim 1, it is characterised in that: in the step 5, utilize
The specific steps that document training sample neural network model is trained are as follows:
Step 51: document training sample being inputted into neural network model, obtains classification results；
Step 52: user selects the true classification results of the document training sample；
Step 53: neural network model is according to the true classification results adjusting parameter；
Step 54: when classification accuracy rate is greater than threshold value, completing training, the neural network model after being trained.
6. a kind of mobile phone safe information generating method according to claim 1, it is characterised in that: the step 4 and step 6
The middle encryption method used is one of SM2 and SM3.
7. a kind of mobile phone safe information storing method, it is characterised in that: the following steps are included:
Step 1: user generates or receives the file information to be identified,
Step 2: judging the file information classification, if photographic intelligence to be identified, then go to step 3, believe if document to be identified
Breath then gos to step 4；
Step 3: by the SVM classifier model after photographic intelligence to be identified input training, classification results are obtained, if classification results are
Sensitive information then encrypts photo, and generates photo security information, and the photo security information is stored in local cipher
Photo stocking system is then stored in non-encrypted photo stocking system if non-sensitive information；
Step 4: by the neural network model after document information to be identified input training, classification results are obtained, if classification results are
Sensitive information then encrypts document, and generates document security information, and the document security information is stored in local cipher
Document stocking system is then stored in non-encrypted document stocking system if non-sensitive information.
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